“Industry 4.0” has become the future direction of manufacturing industry. To prepare for this upgrade, it is important to study the automation of semiconductor failure analysis. In this paper, the sample polishing a...“Industry 4.0” has become the future direction of manufacturing industry. To prepare for this upgrade, it is important to study the automation of semiconductor failure analysis. In this paper, the sample polishing activity was studied for upgrading to a smart polishing process. Two major issues were identified in implementing the smart polishing process: the optimization of current polishing recipes and the capability of making decisions based on live feedback. With the help of Solver add-in, the current polishing recipes were optimized. To make decisions based on live images captured during polishing, strategies were explored based on finger polishing process study. Our investigation showed that a grey scale line profile analysis on images can be used to build the vision capability of our smart polishing system, on which a decision- making capability can be developed.展开更多
首先提出了一种基于主瓶颈设备利用率的并行半导体生产线投料控制策略(Release Control Policy Based on Bottleneck Equipment Utility,RCPBEU):分析投料与主瓶颈设备利用率之间的相关性;通过设定不同的仿真场景,确定主瓶颈设备利用率...首先提出了一种基于主瓶颈设备利用率的并行半导体生产线投料控制策略(Release Control Policy Based on Bottleneck Equipment Utility,RCPBEU):分析投料与主瓶颈设备利用率之间的相关性;通过设定不同的仿真场景,确定主瓶颈设备利用率标准区间值,用于判断每卡待投料工件是否投进该生产线。随后,将瓶颈设备扩展到瓶颈加工区,提出了基于主加工区利用率的并行半导体生产线投料控制策略(Release Control Policy Based on Processing Area Utility,RCPPAU):采用试凑法确定主加工区利用率标准区间值;将主加工区与关系协同投料、主加工区或关系协同投料的方法在仿真系统上进行大量仿真。仿真结果表明,上述方法与固定在制品投料方法相比,出片量、加工周期、准时交货率、紧急工件准时交货率均能得到较大程度的改善。展开更多
文摘“Industry 4.0” has become the future direction of manufacturing industry. To prepare for this upgrade, it is important to study the automation of semiconductor failure analysis. In this paper, the sample polishing activity was studied for upgrading to a smart polishing process. Two major issues were identified in implementing the smart polishing process: the optimization of current polishing recipes and the capability of making decisions based on live feedback. With the help of Solver add-in, the current polishing recipes were optimized. To make decisions based on live images captured during polishing, strategies were explored based on finger polishing process study. Our investigation showed that a grey scale line profile analysis on images can be used to build the vision capability of our smart polishing system, on which a decision- making capability can be developed.
文摘首先提出了一种基于主瓶颈设备利用率的并行半导体生产线投料控制策略(Release Control Policy Based on Bottleneck Equipment Utility,RCPBEU):分析投料与主瓶颈设备利用率之间的相关性;通过设定不同的仿真场景,确定主瓶颈设备利用率标准区间值,用于判断每卡待投料工件是否投进该生产线。随后,将瓶颈设备扩展到瓶颈加工区,提出了基于主加工区利用率的并行半导体生产线投料控制策略(Release Control Policy Based on Processing Area Utility,RCPPAU):采用试凑法确定主加工区利用率标准区间值;将主加工区与关系协同投料、主加工区或关系协同投料的方法在仿真系统上进行大量仿真。仿真结果表明,上述方法与固定在制品投料方法相比,出片量、加工周期、准时交货率、紧急工件准时交货率均能得到较大程度的改善。
